We reverse-engineer your legacy system\u2019s business logic, migrate your data, and replace aging platforms in governed phases -- so your operation never stops during the transition. Denver and the Front Range corridor are home to the densest concentration of aerospace and space manufacturing in the country outside of the Puget Sound. Lockheed Martin's Waterton facility builds missile systems and space vehicles. Ball Aerospace in Boulder produces satellite systems and imaging instruments. Raytheon's Aurora facility develops missile defense components under ITAR controls that restrict which cloud platforms and software vendors they can use. The Anschutz Medical Campus has spawned a biotech manufacturing cluster where Terumo BCT and dozens of smaller device firms operate under FDA 21 CFR requirements that create their own digital transformation friction.
Denver's aerospace manufacturers are caught between ITAR's cloud restrictions and the operational reality that modern supply chain management requires cloud connectivity — navigating that tension is the defining DT challenge for Colorado's defense manufacturers.
Reverse-engineer and document your legacy system\u2019s business logic, data structures, integrations, and dependencies. Create the knowledge base that probably never existed.
Build a modern API layer on top of legacy systems so new applications can integrate without touching the core. Unlock your data without the risk of a full replacement.
Replace modules incrementally -- move order management first, then inventory, then financials. Each phase is self-contained with rollback capability.
Migrate decades of historical data with format conversion, cleansing, and validation. No data left behind, no data corrupted in transit.
Run old and new systems in parallel with automated reconciliation. Switch over only when the new system proves it matches the old one\u2019s accuracy.
Train your team on the new systems and document everything. The goal is independence, not a new dependency on us.
Audit the legacy system -- business logic, data model, integrations, customizations, and tribal knowledge. Identify what must be preserved vs. what can be retired.
Define the modernization path -- API wrapping, module-by-module replacement, or full platform migration. Present risk analysis and timeline for each option.
Build the integration bridge between legacy and modern systems. This alone often delivers immediate value by unlocking data for BI and automation.
Replace legacy modules one at a time with parallel running and validation gates. Each phase is a complete, tested unit before the next begins.
Retire the legacy system with data archival, compliance documentation, and final validation. Clean cutover with no orphaned dependencies.
Legacy System Modernization for Denver aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Legacy System Modernization for Denver medical devices operations - configured around local workflows, data ownership, and implementation governance.
Legacy System Modernization for Denver food & beverage operations - configured around local workflows, data ownership, and implementation governance.
Legacy System Modernization for Denver electronics operations - configured around local workflows, data ownership, and implementation governance.
Legacy System Modernization for Denver technology & software operations - configured around local workflows, data ownership, and implementation governance.
Legacy System Modernization for Denver financial services operations - configured around local workflows, data ownership, and implementation governance.
Yes. We have extensive experience with AS/400 (IBM i) including RPG, COBOL, DB2/400, and green-screen applications. We can build API layers, extract data, and plan phased migrations off the platform.
That\u2019s common and expected. Our discovery phase reverse-engineers business logic from the running system -- examining data flows, testing scenarios, and interviewing users. We document what the system actually does, not what someone thinks it does.
Phased delivery, parallel running, automated reconciliation, and rollback plans at every stage. We never put your operation at risk with a big-bang cutover. If a phase isn\u2019t ready, we don\u2019t force it.
It depends on system complexity. An API layer can be built in 4-8 weeks. A full platform replacement typically takes 6-18 months with phased delivery. We deliver usable value at every phase, not just at the end.
Most manufacturers are still running workflows that require a person to touch every exception, every order, every routing decision. AI agents eliminate that bottleneck — not by replacing your people, but by handling the work that was always below their pay grade.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
Odoo Maintenance captures work orders, failure reasons, repair times, and equipment history. We build AI models on top of that data to identify failure patterns and recommend maintenance windows before breakdowns occur — no new hardware, no IoT infrastructure required.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Most manufacturers price by cost-plus formula or by whatever the sales rep negotiated last time. AI pricing models factor in material costs, competitive positioning, customer segment, order size, inventory position, and market conditions — governed by business rules so every price stays within approved boundaries.
When an order hits your system, someone decides which warehouse ships it — usually based on habit, proximity, or whoever answered the phone. AI order routing makes that decision in real time, optimizing across inventory availability, shipping cost, delivery speed, and warehouse workload.
Manufacturers still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.
Your dealers call or email to check stock before placing orders because they can't see what's available. We give them live ATP visibility across all your warehouses — available, allocated, in-transit, and expected replenishment dates — straight from your ERP and WMS.
We govern cloud migration in phases — every dependency mapped, every workload sequenced, every cutover window defined. Zero-downtime migration for manufacturers who can't afford an outage.
Most manufacturing AI projects die in the pilot phase. We deploy AI that integrates into your actual workflows -- demand forecasting, predictive maintenance, pricing optimization, and intelligent routing -- governed by operational data contracts.
Your demand planning process runs on last year\u2019s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate \u2014 and they improve automatically as more data accumulates.
Your legacy system holds critical data that modern applications need -- but it has no APIs, no webhooks, and no modern integration points. We build a REST/GraphQL API layer on top of your legacy system so new applications can access data without touching the core.
Metrotechs starts with the operating questions: which records are trusted, which workflows are manual, which systems own each decision, and where AI can safely improve throughput.
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